This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, these statements are to be read in this light and are not to be understood as admissions about what is or is not prior art.
Since their emergence in the mid-1980s, mobile mapping systems (MMS) onboard terrestrial and airborne platforms have been established as the most economic and accurate methodology for collecting geospatial data to satisfy a variety of needs including precision agriculture. The MMS' ability to address the demands of such applications is boosted by recent advances in direct georeferencing and imaging technologies. Direct evaluation of mobile platforms' position and orientation using integrated global navigation satellite systems (GNSS) and inertial navigation systems (INS) is allowing for precise mapping using minimal number of ground control points (GCPs). However, in spite of these advances, MMS remains prohibitively costly for many applications and requires extensive technical expertise. In this regard, low-cost unmanned airborne vehicles (UAVs) equipped with directly georeferenced passive- and active-imaging systems are emerging as alternative mobile-mapping platforms that can suit the needs of several applications such as precision agriculture. To that end, UAV-based MMS are gaining tremendous interest from researchers in the mapping and plant-science fields to satisfy needs such as precision agriculture, high-throughput phenotyping, etc.
Precision agriculture has become an important activity for 1) optimizing crop yield given diminishing resources, 2) increasing the reliability of crop-yield prediction, and 3) reducing the agricultural impact on the environment through efficient use of fertilizers and pesticides. Technologies are also being adapted for advanced plant breeding where phenotypic data are obtained to quantify plant growth, structure, and composition at multiple scales over the growing season. Traditional phenotyping has primarily been conducted in field-based plots, which is time-consuming, labor-intensive, and includes destructive sampling.
To obtain a rich set of structure and chemistry based traits, airborne vehicle platforms are being equipped with RGB cameras and hyperspectral scanners. RGB cameras provide high geometric resolution for accurate localization and estimation of important plant traits such as height, canopy closure, and leaf structure. Hyperspectral scanners with fine spectral resolution provide useful data for the estimation of canopy nitrogen, chlorophyll content, and various narrow-band vegetation indices. Due to the volume of collected data, in general, RGB cameras implement frame arrays whereas hyperspectral scanners are most commonly based on a linear array that captures scenes while operating in a push-broom mode (i.e., the scene coverage is achieved through multiple exposures of the linear array during the platform's motion along its trajectory). However, there are no robust methods to correlate these two imagery techniques, requiring both to be obtained simultaneously, which pose a challenge since image capturing for one technique can interfere with the other technique.
There is, therefore an unmet need for a novel approach to correlate RGB and hyperspectral imagery techniques obtained from respective cameras mounted on an airborne vehicle thereby allowing independent image capturing events.